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Nat Biotechnol. 2014 Sep;32(9):926-32. doi: 10.1038/nbt.3001. Epub 2014 Aug 24.

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance.

Author information

1
1] Center for Genomics and Division of Microbiology &Molecular Genetics, School of Medicine, Loma Linda University, Loma Linda, California, USA. [2].
2
1] Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA. [2].
3
1] Microarray and Genome Informatics Group, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA. [2] Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA. [3].
4
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.
5
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA.
6
The Office of Scientific Coordination, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA.
7
Functional Genomics Core, Department of Molecular Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA.
8
Chair of Bioinformatics Research Group, Boku University Vienna, Vienna, Austria.
9
1] Chair of Bioinformatics Research Group, Boku University Vienna, Vienna, Austria. [2] University of Warwick, Coventry, UK.
10
CMINDS Research Center, Department of Electrical and Computer Engineering, Francis College of Engineering, University of Massachusetts, Lowell, Massachusetts, USA.
11
Department of Toxicogenomics, Maastricht University, Maastricht, the Netherlands.
12
Australian Genome Research Facility Ltd., The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia.
13
AbbVie, Inc., North Chicago, Illinois, USA.
14
Research Informatics and Statistics, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA.
15
Thomson Reuters, IP &Science, Carlsbad, California, USA.
16
Vavilov Institute of General Genetics, Russian Academy of Science, Moscow, Russia.
17
Fondazione Bruno Kessler, Trento, Italy.
18
1] Fondazione Bruno Kessler, Trento, Italy. [2] Computational Biology Department, Research and Innovation Centre, Fondazione Edmund Mach (FEM), San Michele all'Adige, Italy.
19
Bioinformatics core, Department of Pathology, University of North Dakota, Grand Forks, North Dakota, USA.
20
1] Microarray and Genome Informatics Group, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA. [2] Kelly Government Solutions, Inc., Durham, North Carolina, USA.
21
Biomolecular Screening Branch, Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
22
SRA International, Durham, North Carolina, USA.
23
Division of Systems Biology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA.
24
Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, Illinois, USA.
25
1] Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA. [2] State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Schools of Life Sciences and Pharmacy, Fudan University, Shanghai, China (L.S.'s primary affiliation).
26
Laboratory of Toxicology and Pharmacology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.

Abstract

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R(2)0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

PMID:
25150839
PMCID:
PMC4243706
DOI:
10.1038/nbt.3001
[Indexed for MEDLINE]
Free PMC Article

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